Introduction to Linear Regression Analysis, 6ed, An Indian Adaptation
ISBN: 9789357461283
680 pages
For more information write to us at: acadmktg@wiley.com
Description
This Indian adaptation of the sixth edition of the book builds on the conceptual strength and problem-solving approach of the original text to provide the best-suited content for Indian students. In line with the ethos of the original work, the text continues to provide a significant number of examples and problems with the modification of the existing content. More than a third of these examples and problems have been newly added or revised.
Chapter 1. INTRODUCTION
1.1 Regression and Model Building
1.2 Data Collection
1.3 Uses of Regression
1.4 Role of the Computer
Chapter 2. SIMPLE LINEAR REGRESSION
2.1 Simple Linear Regression Model
2.2 Least-Squares Estimation of the Parameters
2.3 Hypothesis Testing on the Slope and Intercept
2.4 Interval Estimation in Simple Linear Regression
2.5 Prediction of New Observations
2.6 Coefficient of Determination
2.7 A Service Industry Application of Regression
2.8 Does Pitching Win Baseball Games?
2.9 Using SAS® and R for Simple Linear Regression
2.10 Some Considerations in the Use of Regression
2.11 Regression Through the Origin
2.12 Estimation by Maximum Likelihood
2.13 Case Where the Regressor x Is Random
Chapter 3. MULTIPLE LINEAR REGRESSION
3.1 Multiple Regression Models
3.2 Estimation of the Model Parameters
3.3 Hypothesis Testing in Multiple Linear Regression
3.4 Confidence Intervals in Multiple Regression
3.5 Prediction of New Observations
3.6 A Multiple Regression Model for the Patient Satisfaction Data
3.7 Does Pitching and Defense Win Baseball Games?
3.8 Using SAS and R for Basic Multiple Linear Regression
3.9 Hidden Extrapolation in Multiple Regression
3.10 Standardized Regression COEFFICIENTS
3.11 Multicollinearity
3.12 Why do Regression Coefficients Have the Wrong Sign?
Chapter 4. MODEL ADEQUACY CHECKING
4.1 Introduction
4.2 Residual Analysis
4.3 Press Statistic
4.4 Detection and Treatment of Outliers
4.5 Lack of Fit of the Regression Model
Chapter 5. TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES
5.1 Introduction
5.2 Variance-Stabilizing Transformations
5.3 Transformations to Linearize the Model
5.4 Analytical Methods for Selecting a Transformation
5.5 Generalized and Weighted Least Squares
5.6 Regression Models with Random Effects
Chapter 6. DIAGNOSTICS FOR LEVERAGE AND INFLUENCE
6.1 Importance of Detecting Influential Observations
6.2 Leverage
6.3 Measures of Influence: Cook’s D
6.4 Measures of Influence: DFFITS and DFBETAS
6.5 A Measure of Model Performance
6.6 Detecting Groups of Influential Observations
6.7 Treatment of Influential Observations
Chapter 7. POLYNOMIAL REGRESSION MODELS
7.1 Introduction
7.2 Polynomial Models in One Variable
7.3 Nonparametric Regression
7.4 Polynomial Models in Two or More Variables
7.5 Orthogonal Polynomials
Chapter 8. INDICATOR VARIABLES
8.1 General Concept of Indicator Variables
8.2 Comments on the Use of Indicator Variables
8.3 Regression Approach to Analysis of Variance
Chapter 9. MULTICOLLINEARITY
9.1 Introduction
9.2 Sources of Multicollinearity
9.3 Effects of Multicollinearity
9.4 Multicollinearity Diagnostics
9.5 Methods for Dealing with Multicollinearity
9.6 Using SAS and Python to Perform Ridge and Principal-Component Regression
Chapter 10. VARIABLE SELECTION AND MODEL BUILDING
10.1 Introduction
10.2 Computational Techniques for Variable Selection
10.3 Strategy for Variable Selection and Model Building
10.4 Case Study: Gorman and Toman Asphalt Data Using SAS
Chapter 11. VALIDATION OF REGRESSION MODELS
11.1 Introduction
11.2 Validation Techniques
11.3 Data from Planned Experiments
Chapter 12. INTRODUCTION TO NONLINEAR REGRESSION
12.1 Linear and Nonlinear Regression Models
12.2 Origins of Nonlinear Models
12.3 Nonlinear Least Squares
12.4 Transformation to a Linear Model
12.5 Parameter Estimation in a Nonlinear System
12.6 Statistical Inference in Nonlinear Regression
12.7 Examples of Nonlinear Regression Models
12.8 Using SAS and R to Perform Nonlinear Regression
Chapter 13. GENERALIZED LINEAR MODELS
13.1 Introduction
13.2 Logistic Regression Models
13.3 Poisson Regression
13.4 The Generalized Linear Model
Chapter 14. REGRESSION ANALYSIS OF TIME SERIES DATA
14.1 Introduction to Regression Models for Time Series Data
14.2 Detecting Autocorrelation: The Durbin–Watson Test
14.3 Estimating the Parameters in Time Series Regression Models
14.4 Using R to Time Series Data
Chapter 15. OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS
15.1 Robust Regression
15.2 Effect of Measurement Errors in the Regressors
15.3 Inverse Estimation—The Calibration Problem
15.4 Bootstrapping in Regression
15.5 Classification and Regression Trees (CART)
15.6 Neural Networks
15.7 Missing Data in Regression
15.8 Designed Experiments for Regression